Prognostic Marker Sets For Prostate Cancer

ABSTRACT

Prostate cancer marker sets consisting of particular genes differentially expressed in prostate tumours provide improved accuracy of prostate cancer prognosis. The prostate cancer marker sets of the present invention, one of which consists of 30 genes related to apoptosis, one of which consists of 22 genes related to cell cycle and one of which consists of 30 genes related to response to external stimulus, may be used in a clinical setting to provide information about the likelihood of a prostate cancer patient to survive without treatment (i.e. whether the prostate tumour is “good” or “bad”).

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of United States Provisional Patent Application U.S. Ser. No. 61/452,439 filed Mar. 14, 2011, the entire contents of which is herein incorporated by reference.

FIELD OF THE INVENTION

The present invention is related to prostate cancer, more particularly to methods and markers for predicting prostate cancer risk.

BACKGROUND OF THE INVENTION

There has been significant effort in the past directed to the diagnosis of prostate cancer. The well known prostate specific antigen (PSA) test is one diagnostic test. Another test (Belacel 2010) describes the use of eight different marker genes for diagnosing prostate cancer. Although a variety of tests have been developed for diagnosing prostate cancer, there have been relatively few efforts directed to developing prognostic tests for predicting low-risk patients in order to determine the proper treatment regime for patients diagnosed with prostate cancer. Two large scale studies of prostate cancer recently showed that there is significant over-diagnosis and overtreatment of prostate cancer patients (Andriole 2009; Schröder 2009). Many prostate cancer patients suffer from the side effects of treatment and society is bearing the related costs. Most of these treatments are unnecessary.

Recently, an algorithm (Multiple Survival Screening (MSS)) has been developed for identifying high-quality cancer prognostic markers and this algorithm was applied for identifying robust marker sets for breast cancer prognosis (Li 2010; Wang 2010).

There is a need to find new markers and develop new tests which are able to more accurately predict low-risk patients for prostate cancer who should receive little or no treatment.

SUMMARY OF THE INVENTION

It has now been found that prostate cancer marker sets consisting of particular genes differentially expressed in prostate tumours advantageously provide improved accuracy of prostate cancer prognosis. The prostate cancer marker sets of the present invention, one of which consists of 30 genes related to apoptosis, one of which consists of 22 genes related to cell cycle and one of which consists of 30 genes related to response to external stimulus, may be used in a clinical setting to provide information about the likelihood of a prostate cancer patient to survive without treatment (i.e. whether the prostate tumour is “good” or “bad”).

In one aspect of the present invention, there is provided a method of assessing likelihood of a patient having a prostate tumour benefiting from prostate cancer treatment, the method comprising: obtaining a sample of the prostate tumour or an extract thereof having message RNA therein of the patient; determining a gene expression profile of the sample for genes of a gene marker set; and, comparing the gene expression profile of the sample to standardized “good” and “bad” profiles of the marker set to determine whether the gene expression profile of the sample predicts that the tumour is “good” or “bad”, wherein “good” indicates that the patient is predicted to be at low-risk and would not likely benefit from prostate cancer treatment, “bad” indicates that the patient is predicted to be at high-risk and would likely benefit from prostate cancer treatment, and the gene marker set is Set 1, Set 2 or Set 3, wherein

Set 1 consists of apoptosis-related genes as follows:

Gene EntrezGene ID Full Name of Gene COL4A3 1285 type IV collagen BIRC5 332 baculoviral IAP repeat containing 5 TOP2A 7153 topoisomerase (DNA) II alpha CDC2 983 cyclin-dependent kinase 1 (CDK1) NRAS 4893 neuroblastoma RAS viral (v-ras) oncogene homolog GAS1 2619 growth arrest-specific 1 LIG4 3981 ligase IV, DNA, ATP-dependent OSM 5008 oncostatin M PML 5371 promyelocytic leukemia TP53 7157 tumour protein p53 NF1 4763 neurofibromin 1 SIAH1 6477 seven in absentia homolog 1 (Drosophila) MALT1 10892 mucosa associated lymphoid tissue lymphoma translocation gene 1 KIT 3815 v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog RHOA 387 ras homolog gene family, member A ESR1 2099 estrogen receptor 1 RARB 5915 retinoic acid receptor, beta VAV1 7409 vav 1 guanine nucleotide exchange factor WRN 7486 Werner syndrome, RecQ helicase-like TNFRSF10A 8797 tumour necrosis factor receptor superfamily, member 10a RIPK1 8737 receptor (TNFRSF)-interacting serine-threonine kinase 1 ABL1 25 c-abl oncogene 1, non-receptor tyrosine kinase TERT 7015 telomerase reverse transcriptase GLI3 2737 GLI family zinc finger 3 JUN 3725 jun proto-oncogene NFKBIA 4792 nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha LCK 3932 lymphocyte-specific protein tyrosine kinase CASP3 836 caspase 3, apoptosis-related cysteine peptidase E2F2 1870 E2F transcription factor 2 LTA 4049 lymphotoxin alpha (TNF superfamily, member 1) Set 2 consists of cell cycle-related genes as follows:

Gene Name EntrezGene ID Description BCL2 596 B-cell CLL/lymphoma 2 RAD51 5888 RAD51 homolog (RecA homolog, E. coli) (S. cerevisiae) CDKN2B 1030 cyclin-dependent kinase inhibitor 2B (p15, inhibits CDK4) GML 2765 glycosylphosphatidylinositol anchored molecule like protein E2F1 1869 E2F transcription factor 1 IKZF1 10320 IKAROS family zinc finger 1 (Ikaros) BLM 641 Bloom syndrome, RecQ helicase-like ABL1 25 c-abl oncogene 1, non-receptor tyrosine kinase LIG4 3981 ligase IV, DNA, ATP-dependent CCNA2 890 cyclin A2 NUMA1 4926 nuclear mitotic apparatus protein 1 CCNC 892 cyclin C RBL2 5934 retinoblastoma-like 2 (p130) LTA 4049 lymphotoxin alpha (TNF superfamily, member 1) ERCC2 2068 excision repair cross-complementing rodent repair deficiency, complementation group 2 CASP3 836 caspase 3, apoptosis-related cysteine peptidase TP53 7157 tumour protein p53 RAD54L 8438 RAD54-like (S. cerevisiae) CCND3 896 cyclin D3 WEE1 7465 WEE1 homolog (S. pombe) BIRC5 332 baculoviral IAP repeat containing 5 HDAC1 3065 histone deacetylase 1 Set 3 consists of response to external stimulus-related genes as follows:

Gene Name EntrezGene ID Description COL4A3 1285 Type IV collagen TOP2A 7153 topoisomerase (DNA) II alpha CDC2 983 cyclin-dependent kinase 1 (CDK1) LYN 4067 v-yes-1 Yamaguchi sarcoma viral related oncogene homolog PXN 5829 paxillin NTRK3 4916 neurotrophic tyrosine kinase, receptor, type 3 PDGFRA 5156 platelet-derived growth factor receptor, alpha polypeptide NRAS 4893 neuroblastoma RAS viral (v-ras) oncogene homolog CHEK1 1111 CHK1 checkpoint homolog (S. pombe) PARP1 142 poly (ADP-ribose) polymerase 1 KIT 3815 v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog TGFBR3 7049 transforming growth factor, beta receptor III CCNA2 890 cyclin A2 NF1 4763 neurofibromin 1 MAPK10 5602 mitogen-activated protein kinase 10 CD9 928 CD9 molecule ESR1 2099 estrogen receptor 1 FRAP1 2475 mechanistic target of rapamycin (serine/threonine kinase) (MTOR) PML 5371 promyelocytic leukemia ABL1 25 c-abl oncogene 1, non-receptor tyrosine kinase TP53 7157 tumour protein p53 LIG4 3981 ligase IV, DNA, ATP-dependent WEE1 7465 WEE1 homolog (S. pombe SYK 6850 spleen tyrosine kinase MALT1 10892 mucosa associated lymphoid tissue lymphoma translocation gene 1 PTCH1 5727 patched 1 CASP3 836 caspase 3, apoptosis-related cysteine peptidase BLM 641 Bloom syndrome, RecQ helicase-like FYN 2534 FYN oncogene related to SRC, FGR, YES WRN 7486 Werner syndrome, RecQ helicase-like

The genes in the prostate cancer marker sets of the present invention are individually known and are individually known to be differentially expressed in prostate tumour cells. How they are differentially expressed and whether their differential expression generally correlates to “good” or “bad” tumours can also be determined from publicly available datasets. However, the specific combination of the genes in each marker set of the present invention unexpectedly provides for more robust marker sets having improved prognostic accuracy for prostate cancer survival. The marker sets of the present invention consisting of the specific combination of genes that gives rise to the improved prognostic accuracy may be generated using the Multiple Survival Screening (MSS) method previously developed (Li 2010; Wang 2010).

The sample comprises a sample of the prostate tumour of the patient or an extract thereof, which contains the genes in the marker set or message RNA that hybridizes to the genes in the marker set. Preferably, the sample comprises a sample of the prostate tumour of the patient.

Preferably, all three sets are used together to make risk predictions. Thus, gene expression profiles of the sample are preferably determined for the genes in each of Sets 1, 2 and 3. In this case, the gene expression profiles are compared to standardized “good” and “bad” profiles of each respective gene marker set to determine whether each of the gene expression profiles predicts that the tumour is “good” or “bad”. If all three marker sets predict that the tumour is “good” then the patient is predicted to be at low-risk and would not likely benefit from prostate cancer treatment. If all three marker sets predict that the tumour is “bad” then the patient is predicted to be at high-risk and would likely benefit from prostate cancer treatment. If one or two of the marker sets predict that the tumour is “good” or one or two of the marker sets predict that the tumour is “bad” then the patient is predicted to be at intermediate-risk and may or may not benefit from prostate cancer treatment. Using all three marker sets improves accuracy of the prognosis.

In a particular embodiment, each gene in the gene expression profile has a gene expression value and a modified gene expression profile is obtained by multiplying the gene expression value by its marker-factor. Standardized “good” and “bad” profiles are determined by computing standardized centroids for both “good” and “bad” classes using prediction analysis for microarrays method (Tibshirani 2002). Modified class centroids of the marker set are obtained by multiplying the standardized centroids for each class by the marker-factor. The modified gene expression profile of the sample is compared to each modified class centroid to determine the tumour is “good” or “bad”. The class whose centroid is closest to the modified gene expression profile, in Pearson correlation distance, is predicted to be the class for the sample.

Gene expression profiles of a patient's prostate tumour may be readily obtained by any number of methods known in the art, for example microarray analysis, individual gene screening, etc. In a preferred embodiment, the sample is screened that against a microarray on which gene probes of the marker sets are printed. An output of the gene expression profile of the sample is preferably obtained before comparing the gene expression profile to the standardized “good” and “bad” profiles of the marker set. To obtain the output, message RNA in the sample may be hybridized to the genes on the microarray, the hybridized microarray may be scanned to get all the readouts of marker genes for the sample, the readouts may be normalized and the gene expression profile of the marker set for the sample is thereby obtained. Detailed information for making microarray gene chip, scanning and normalization of array data is generally known in the art and can be found in the publicly available literature (http://en.wikipedia.org/wiki/DNA_microarray). It is also possible to obtain the gene expression profile by RNA-sequencing and related sequencing technologies as these technologies become more accessible (http://en.wikipedia.org/wiki/RNA-Seq).

In another embodiment, kits or commercial packages are provided, which comprise gene probes for each of the genes in a gene marker set of the present invention along with instructions for obtaining a gene expression profile of a sample for the gene marker set. The kit or commercial package may further comprise instructions for comparing the gene expression profile of the sample to standardized “good” and “bad” profiles of the marker set to determine whether the gene expression profile of the sample predicts that the tumour is “good” or “bad”. Preferably, the kit or commercial package comprises gene probes for all three gene marker sets of the present invention. The kit or commercial package may further comprise means for obtaining a sample of a prostate tumour having message RNA therein from a patient, for example suitable syringes, fluid and/or tissue separation means, etc. In addition to the gene probes, the kit or commercial package may further comprise reagents and/or equipment useful for screening the sample against the gene probes for obtaining the gene expression profile of the sample. Various standard elements of such kits or commercial packages are generally known in the art.

Further features of the invention will be described or will become apparent in the course of the following detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

In order that the invention may be more clearly understood, embodiments thereof will now be described in detail by way of example, with reference to the accompanying drawings, in which:

FIG. 1A provides gene names and EntrezGene ID numbers for genes in the GSE10645 prostate cancer gene expression dataset which are deposited in a public database (http://www.ncbi.nlm.nih.gov/geo/) that belong to apoptosis GO term;

FIG. 1B provides gene names and EntrezGene ID numbers for genes in the GSE10645 prostate cancer gene expression dataset that belong to cell cycle GO term; and,

FIG. 1C provides gene names and EntrezGene ID numbers for genes in the GSE10645 prostate cancer gene expression dataset that belong to response to external stimulus GO term.

DESCRIPTION OF PREFERRED EMBODIMENTS Example 1 Generation of Prostate Cancer Marker Sets

To develop the prostate cancer marker sets of the present invention, the Multiple Survival Screening (MSS) method (Li 2010; Wang 2010) was used. In applying this method, a training set of 189 samples was selected from the GSE10645 GEO dataset (Nakagawa 2008). This prostate cancer gene expression datatset is from the population-based Swedish-Watchful Waiting cohort. The cohort consists of men with localized prostate cancer (clinical stage T1-T2, Mx, NO). The GSE10645 GEO dataset contains information about genes that are differentially expressed in prostate tumours. The dataset identifies whether each of these genes is up-regulated or down-regulated in tumours and correlates these genes to patient survival (i.e. “good” vs. “bad” tumours).

The 189 samples from GSE10645 were randomly divided into three groups of 63 samples, each group retaining the same proportion of “good” vs. “bad” tumours that was identified in the original GSE10645 dataset. Array-wide screening of the genes was performed on each of the three groups as described in the art (Li 2010; Wang 2010) to obtain survival genes, which are genes whose differential expression values are correlated with prostate cancer patient survivals. It is not relevant whether the expression of each gene is upregulated or downregulated so long as the differential expression is correlated to patient survival. Merging the results from each of the three groups yielded a survival gene set, which includes 133 survival genes.

Using the survival gene set, Gene Ontology (GO) analysis (using GO annotation software, David, http://david.abcc.ncifcrf.gov/) was performed to identify only those genes that belong to GO terms that are known to be associated with prostate cancer, such as apoptosis (cell death), cell adhesion, cell cycle, phosphorylation, response to external stimulus, cell motility and cell assembly. Table 1 lists the cancer-related GO term gene sets. One million distinct random-gene-sets were generated by randomly picking 30 genes from each cancer-related GO term gene set.

TABLE 1 GO Term Number of genes Apoptosis 47 Cell adhesion 68 Cell cycle 36 Phosphorylation 72 Response to external stimulus 67 Cell motility 49 Cell assembly 67

Of the 189 samples selected from the GSE10645 GEO dataset to form the training set, 36 random datasets were generated by randomly picking 60 samples from the training set while retaining in each random dataset the same proportion of “good” vs. “bad” tumours that was identified in the original GSE10645 dataset.

For a given GO term gene set, survival screening was then conducted using the 1 million random-gene-sets against all the 36 random datasets. For each random dataset, the statistical significance of the correlation between the expression values of each random-gene-set (30 genes) and patient survival status (“good” or “bad”) was examined by Kaplan-Meier analysis by implementing the Cox-Mantel log-rank test (Cui 2007). If the P value was less than a cut-off for a survival screening using one random-gene-set against one random dataset, that random-gene-set was said to have passed. When a few thousands of random-gene-sets had passed 32 or more random datasets (the detailed parameters are shown in Table 5), the random-gene-sets that had passed were retained for further analysis. The genes in the retained random-gene-sets were then ranked based on their frequency of appearance in the passed random-gene-sets. The top 30 genes were chosen as a potential-marker-set. A similar survival screening of random-gene-sets against random datasets was performed for each of the other selected GO term gene sets.

For each GO term gene set another 1 million distinct random-gene-sets were generated and the survival screening process using the random datasets mentioned above was repeated. If the gene members for the top 30 were substantially the same as those in the potential-marker-set generated by the first screening, then the potential-marker-set is stable and can be used as a real prostate cancer marker set. If the genes for the two potential marker sets were not substantially the same, then these GO term genes are unsuitable for finding a real marker set and the potential marker set was dropped from further analysis. In some cases somewhat fewer than 30 genes may be the same in the two potential marker sets, in which case the smaller set may be designated as a marker set.

In this way, three prostate cancer marker sets were generated having stable signatures, one related to apoptosis, one related to cell cycle and one related to response to external stimulus. The genes, EntrezGene ID and full names of the genes in each of the three marker sets are given in the Tables 2-4 below. More details of each gene, including the nucleotide sequence of each gene, are known in the art and may be conveniently found in the National Center for Biotechnology Information (NCBI) Databases at http://www.ncbi.nlm.nih.gov/.

TABLE 2 Marker Set Related to Apoptosis (30 genes) Gene EntrezGene ID Full Name of Gene COL4A3 1285 type IV collagen BIRC5 332 baculoviral IAP repeat containing 5 TOP2A 7153 topoisomerase (DNA) II alpha CDC2 983 cyclin-dependent kinase 1 (CDK1) NRAS 4893 neuroblastoma RAS viral (v-ras) oncogene homolog GAS1 2619 growth arrest-specific 1 LIG4 3981 ligase IV, DNA, ATP-dependent OSM 5008 oncostatin M PML 5371 promyelocytic leukemia TP53 7157 tumour protein p53 NF1 4763 neurofibromin 1 SIAH1 6477 seven in absentia homolog 1 (Drosophila) MALT1 10892 mucosa associated lymphoid tissue lymphoma translocation gene 1 KIT 3815 v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog RHOA 387 ras homolog gene family, member A ESR1 2099 estrogen receptor 1 RARB 5915 retinoic acid receptor, beta VAV1 7409 vav 1 guanine nucleotide exchange factor WRN 7486 Werner syndrome, RecQ helicase-like TNFRSF10A 8797 tumour necrosis factor receptor superfamily, member 10a RIPK1 8737 receptor (TNFRSF)-interacting serine-threonine kinase 1 ABL1 25 c-abl oncogene 1, non-receptor tyrosine kinase TERT 7015 telomerase reverse transcriptase GLI3 2737 GLI family zinc finger 3 JUN 3725 jun proto-oncogene NFKBIA 4792 nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha LCK 3932 lymphocyte-specific protein tyrosine kinase CASP3 836 caspase 3, apoptosis-related cysteine peptidase E2F2 1870 E2F transcription factor 2 LTA 4049 lymphotoxin alpha (TNF superfamily, member 1)

TABLE 3 Marker Set Related to Cell Cycle (22 genes) Gene Name EntrezGene ID Description BCL2 596 B-cell CLL/lymphoma 2 RAD51 5888 RAD51 homolog (RecA homolog, E. coli) (S. cerevisiae) CDKN2B 1030 cyclin-dependent kinase inhibitor 2B (p15, inhibits CDK4) GML 2765 glycosylphosphatidylinositol anchored molecule like protein E2F1 1869 E2F transcription factor 1 IKZF1 10320 IKAROS family zinc finger 1 (Ikaros) BLM 641 Bloom syndrome, RecQ helicase-like ABL1 25 c-abl oncogene 1, non-receptor tyrosine kinase LIG4 3981 ligase IV, DNA, ATP-dependent CCNA2 890 cyclin A2 NUMA1 4926 nuclear mitotic apparatus protein 1 CCNC 892 cyclin C RBL2 5934 retinoblastoma-like 2 (p130) LTA 4049 lymphotoxin alpha (TNF superfamily, member 1) ERCC2 2068 excision repair cross-complementing rodent repair deficiency, complementation group 2 CASP3 836 caspase 3, apoptosis-related cysteine peptidase TP53 7157 tumour protein p53 RAD54L 8438 RAD54-like (S. cerevisiae) CCND3 896 cyclin D3 WEE1 7465 WEE1 homolog (S. pombe) BIRC5 332 baculoviral IAP repeat containing 5 HDAC1 3065 histone deacetylase 1

TABLE 4 Marker Set Related to Response to External Stimulus (30 genes) Gene Name EntrezGene ID Description COL4A3 1285 Type IV collagen TOP2A 7153 topoisomerase (DNA) II alpha CDC2 983 cyclin-dependent kinase 1 (CDK1) LYN 4067 v-yes-1 Yamaguchi sarcoma viral related oncogene homolog PXN 5829 paxillin NTRK3 4916 neurotrophic tyrosine kinase, receptor, type 3 PDGFRA 5156 platelet-derived growth factor receptor, alpha polypeptide NRAS 4893 neuroblastoma RAS viral (v-ras) oncogene homolog CHEK1 1111 CHK1 checkpoint homolog (S. pombe) PARP1 142 poly (ADP-ribose) polymerase 1 KIT 3815 v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog TGFBR3 7049 transforming growth factor, beta receptor III CCNA2 890 cyclin A2 NF1 4763 neurofibromin 1 MAPK10 5602 mitogen-activated protein kinase 10 CD9 928 CD9 molecule ESR1 2099 estrogen receptor 1 FRAP1 2475 mechanistic target of rapamycin (serine/threonine kinase) (MTOR) PML 5371 promyelocytic leukemia ABL1 25 c-abl oncogene 1, non-receptor tyrosine kinase TP53 7157 tumour protein p53 LIG4 3981 ligase IV, DNA, ATP-dependent WEE1 7465 WEE1 homolog (S. pombe) SYK 6850 spleen tyrosine kinase MALT1 10892 mucosa associated lymphoid tissue lymphoma translocation gene 1 PTCH1 5727 patched 1 CASP3 836 caspase 3, apoptosis-related cysteine peptidase BLM 641 Bloom syndrome, RecQ helicase-like FYN 2534 FYN oncogene related to SRC, FGR, YES WRN 7486 Werner syndrome, RecQ helicase-like

TABLE 5 Parameters for Screening of the Marker Sets Number of Passed Number of Passed Sample Sets Gene Sets Cut-off P value Apoptosis 32 4674 0.00001 Cell cycle 32 5548 0.0001 Response to 35 4142 0.00001 external stimulus

Example 2 Validating Effectiveness of the Marker Sets in Prostate Cancer Prognosis

The effectiveness of the three marker sets generated in Example 1 was validated against three separate GEO datasets containing prostate cancer gene expression data from sample populations. One of the three datasets against which the markers were validated was the GSE16560 dataset described above except that 261 samples from that dataset were used. The other two test datasets were GEO datasets GSE21034 (Taylor 2010) and GSE10645 (Nakagawa 2008, the validation samples marked by the authors). In all three cases, test datasets were constructed by selecting samples from the GEO datasets so that the test datasets contained 90% “good” tumours and 10% “bad” tumours, based on ultimate patient survival outcomes, in order to simulate the suggestion that over 90% of prostate cancer patients do not actually need to be treated.

To perform the validation for a given test dataset containing ‘n’ samples, the gene expression profile of the marker set was extracted. For each gene expression value its marker-factor was multiplied to obtain a modified gene expression profile of the testing sample. Standardized centroids were computed for both “good” and “bad” classes from n−1 samples for the marker set using the Prediction Analysis for Microarrays (PAM) method (Tibshirani 2002). The marker-factor of each gene was multiplied to the class centroids to get modified class centroids of the marker set. For predicting the recurrence of the targeted testing sample using the marker set the modified gene expression profile of the sample was compared to each of these modified class centroids. The class whose centroid that it is closest to, in Pearson correlation distance, is the predicted class for that sample. If the sample is predicted to be a “good” tumour, it is denoted as 0, otherwise it is denoted as 1. If all three marker sets predict that a particular prostate cancer sample is “good” (i.e. denoted as 0 for all 3 marker sets), the sample is assigned to low-risk group.

If all three marker sets predict that a particular prostate cancer sample is “bad” (i.e. denoted as 1 for all 3 marker sets), the sample is assigned to high-risk group. If a sample is not assigned to low-risk or high-risk group, it is assigned to intermediate-risk group.

This validation process was carried out in all three of the test datasets. Table 6 shows the results for the low-risk group in comparison to the GSE16560 training set originally used to generate the three marker sets (see Example 1). As would be expected, the accuracy of the marker sets against the training set is 100%. The accuracy of the marker sets against the test datasets derived from the three GEO datasets is remarkably high.

TABLE 6 Predicting Accuracy of the Marker Sets Dataset No. of Samples Accuracy (low-risk group) GSE10645 (training set) 189   100% GSE16560 261 95.58% GSE21034 140 99.31% GSE10645 (the validation 205 98.24% samples marked by the authors, Nakagawa 2008)

The accuracy of the present marker sets can be compared to the prior art. Table 7 provides the performance of several markers and marker sets of the prior art. Table 7 is derived from Table 5 of Nakagawa 2008. The clinical models used and the nature of the various markers and marker sets listed in Table 7 below are explained in Nakagawa 2008. It is clear comparing Table 6 to Table 7 that the prognostic accuracy of the present marker sets for determining the expected survival of a prostate cancer patient is substantially greater than the prior art markers and marker sets.

TABLE 7 AUC's of Prior Art Markers and Marker Sets Probes Clinical model Marker or Marker Set alone A B C Clinical model alone NA 0.736 0.757 0.783 Nakagawa 2008 - Final 17 gene/probe 0.852 0.857 0.873 0.883 Glinsky 2004 - Signature 1 0.665 0.762 0.776 0.798 Glinsky 2004 - Signature 2 0.638 0.764 0.781 0.798 Glinsky 2004 - Signature 3 0.669 0.770 0.788 0.810 Glinksy 2005 0.729 0.780 0.800 0.811 Lapointe 2004 - Tumor Recurrence Sig. 0.789 0.825 0.838 0.855 Lapointe 2004 - MUC1 and AZGP1 0.660 0.767 0.777 0.793 Singh 2002 0.783 0.824 0.838 0.851 Yu 2004 0.725 0.797 0.815 0.830

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Other advantages that are inherent to the structure are obvious to one skilled in the art. The embodiments are described herein illustratively and are not meant to limit the scope of the invention as claimed. Variations of the foregoing embodiments will be evident to a person of ordinary skill and are intended by the inventor to be encompassed by the following claims. 

1. A method of assessing likelihood of a patient having a prostate tumour benefiting from prostate cancer treatment, the method comprising: obtaining a sample of the prostate tumour or an extract thereof having message RNA therein of the patient; determining a gene expression profile of the sample for genes of a gene marker set; and, comparing the gene expression profile of the sample to standardized “good” and “bad” profiles of the marker set to determine whether the gene expression profile of the sample predicts that the tumour is “good” or “bad”, wherein “good” indicates that the patient is predicted to be at low-risk and would not likely benefit from prostate cancer treatment, “bad” indicates that the patient is predicted to be at high-risk and would likely benefit from prostate cancer treatment, and the gene marker set is Set 1, Set 2 or Set 3, wherein Set 1 consists of apoptosis-related genes as follows: Gene EntrezGene ID Full Name of Gene COL4A3 1285 type IV collagen BIRC5 332 baculoviral IAP repeat containing 5 TOP2A 7153 topoisomerase (DNA) II alpha CDC2 983 cyclin-dependent kinase 1 (CDK1) NRAS 4893 neuroblastoma RAS viral (v-ras) oncogene homolog GAS1 2619 growth arrest-specific 1 LIG4 3981 ligase IV, DNA, ATP-dependent OSM 5008 oncostatin M PML 5371 promyelocytic leukemia TP53 7157 tumour protein p53 NF1 4763 neurofibromin 1 SIAH1 6477 seven in absentia homolog 1 (Drosophila) MALT1 10892 mucosa associated lymphoid tissue lymphoma translocation gene 1 KIT 3815 v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog RHOA 387 ras homolog gene family, member A ESR1 2099 estrogen receptor 1 RARB 5915 retinoic acid receptor, beta VAV1 7409 vav 1 guanine nucleotide exchange factor WRN 7486 Werner syndrome, RecQ helicase-like TNFRSF10A 8797 tumour necrosis factor receptor superfamily, member 10a RIPK1 8737 receptor (TNFRSF)-interacting serine-threonine kinase 1 ABL1 25 c-abl oncogene 1, non-receptor tyrosine kinase TERT 7015 telomerase reverse transcriptase GLI3 2737 GLI family zinc finger 3 JUN 3725 jun proto-oncogene NFKBIA 4792 nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha LCK 3932 lymphocyte-specific protein tyrosine kinase CASP3 836 caspase 3, apoptosis-related cysteine peptidase E2F2 1870 E2F transcription factor 2 LTA 4049 lymphotoxin alpha (TNF superfamily, member 1)

Set 2 consists of cell cycle-related genes as follows: Gene Name EntrezGene ID Description BCL2 596 B-cell CLL/lymphoma 2 RAD51 5888 RAD51 homolog (RecA homolog, E. coli) (S. cerevisiae) CDKN2B 1030 cyclin-dependent kinase inhibitor 2B (p15, inhibits CDK4) GML 2765 glycosylphosphatidylinositol anchored molecule like protein E2F1 1869 E2F transcription factor 1 IKZF1 10320 IKAROS family zinc finger 1 (Ikaros) BLM 641 Bloom syndrome, RecQ helicase-like ABL1 25 c-abl oncogene 1, non-receptor tyrosine kinase LIG4 3981 ligase IV, DNA, ATP-dependent CCNA2 890 cyclin A2 NUMA1 4926 nuclear mitotic apparatus protein 1 CCNC 892 cyclin C RBL2 5934 retinoblastoma-like 2 (p130) LTA 4049 lymphotoxin alpha (TNF superfamily, member 1) ERCC2 2068 excision repair cross-complementing rodent repair deficiency, complementation group 2 CASP3 836 caspase 3, apoptosis-related cysteine peptidase TP53 7157 tumour protein p53 RAD54L 8438 RAD54-like (S. cerevisiae) CCND3 896 cyclin D3 WEE1 7465 WEE1 homolog (S. pombe) BIRC5 332 baculoviral IAP repeat containing 5 HDAC1 3065 histone deacetylase 1

Set 3 consists of response to external stimulus-related genes as follows: Gene Name EntrezGene ID Description COL4A3 1285 Type IV collagen TOP2A 7153 topoisomerase (DNA) II alpha CDC2 983 cyclin-dependent kinase 1 (CDK1) LYN 4067 v-yes-1 Yamaguchi sarcoma viral related oncogene homolog PXN 5829 paxillin NTRK3 4916 neurotrophic tyrosine kinase, receptor, type 3 PDGFRA 5156 platelet-derived growth factor receptor, alpha polypeptide NRAS 4893 neuroblastoma RAS viral (v-ras) oncogene homolog CHEK1 1111 CHK1 checkpoint homolog (S. pombe) PARP1 142 poly (ADP-ribose) polymerase 1 KIT 3815 v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog TGFBR3 7049 transforming growth factor, beta receptor III CCNA2 890 cyclin A2 NF1 4763 neurofibromin 1 MAPK10 5602 mitogen-activated protein kinase 10 CD9 928 CD9 molecule ESR1 2099 estrogen receptor 1 FRAP1 2475 mechanistic target of rapamycin (serine/threonine kinase) (MTOR) PML 5371 promyelocytic leukemia ABL1 25 c-abl oncogene 1, non-receptor tyrosine kinase TP53 7157 tumour protein p53 LIG4 3981 ligase IV, DNA, ATP-dependent WEE1 7465 WEE1 homolog (S. pombe SYK 6850 spleen tyrosine kinase MALT1 10892 mucosa associated lymphoid tissue lymphoma translocation gene 1 PTCH1 5727 patched 1 CASP3 836 caspase 3, apoptosis-related cysteine peptidase BLM 641 Bloom syndrome, RecQ helicase-like FYN 2534 FYN oncogene related to SRC, FGR, YES WRN 7486 Werner syndrome, RecQ helicase-like


2. The method according to claim 1, wherein the sample comprises a sample of the prostate tumour of the patient.
 3. The method according to claim 1, wherein gene expression profiles of the sample are determined for the genes in each of Sets 1, 2 and 3 and the gene expression profiles are compared to standardized “good” and “bad” profiles of each respective gene marker set to determine whether each of the gene expression profiles predicts that the tumour is “good” or “bad”, whereby if all three marker sets predict that the tumour is “good” then the patient is predicted to be at low-risk and would not likely benefit from prostate cancer treatment, if all three marker sets predict that the tumour is “bad” then the patient is predicted to be at high-risk and would likely benefit from prostate cancer treatment and if one or two of the marker sets predict that the tumour is “good” or one or two of the marker sets predict that the tumour is “bad” then the patient is predicted to be at intermediate-risk and may or may not benefit from prostate cancer treatment.
 4. The method according to claim 1, wherein each gene in the gene expression profile has a gene expression value and a modified gene expression profile is obtained by multiplying the gene expression value by its marker-factor, the standardized “good” and “bad” profiles are determined by computing standardized centroids for both “good” and “bad” classes using prediction analysis for microarrays method, modified class centroids of the marker set are obtained by multiplying the standardized centroids for each class by the marker-factor, and the modified gene expression profile of the sample is compared to each modified class centroid to determine the tumour is “good” or “bad”, wherein the class whose centroid is closest to the modified gene expression profile, in Pearson correlation distance, is predicted to be the class for the sample.
 5. The method according to claim 1, further comprising obtaining an output of the gene expression profile of the sample before comparing the gene expression profile to the standardized “good” and “bad” profiles of the marker set.
 6. The method according to claim 1, wherein the gene expression profile of the sample is determined by screening the sample against a microarray on which gene probes of the marker set are printed.
 7. Use of one or more of the gene marker sets as defined in claim 1 for predicting prostate cancer risk in a patient having a prostate tumour.
 8. The use according to claim 7, wherein all three of the gene marker sets are used for predicting the prostate cancer risk.
 9. A kit for predicting prostate cancer risk in a patient having a prostate tumour, the kit comprising gene probes for each of the genes in a gene marker set as defined in claim 1 along with instructions for obtaining a gene expression profile of a sample for the gene marker set.
 10. The kit according to claim 9 comprising gene probes for all three gene marker sets as defined in claim
 1. 11. The kit according to claim 9, further comprising instructions for comparing the gene expression profile of the sample to standardized “good” and “bad” profiles of the marker set to determine whether the gene expression profile of the sample predicts that the tumour is “good” or “bad”. 